Body mass index modified the effectiveness of low dose aspirin treatment on frozen-thawed embryo transfer outcome: a propensity score-matched study.

Background: Low-dose aspirin is one of the widely used adjuvants in assisted reproductive technologies with the hope of improving the live birth rate. However, the studies regarding its effects are conflicting. The study aimed to investigate the association between aspirin administration and live birth following frozen-thawed embryo transfer (FET) in patients with different body mass index (BMI). Methods: A retrospective cohort study was performed on 11,993 patients receiving FET treatments. 644 of which received a low-dose aspirin (100 mg/day) during endometrial preparation until 10 weeks after transfer. Propensity score matching was performed to avoid selection biases and potential confounders. Results: The clinical pregnancy rate and live birth rate were similar before matching (54.4% versus 55.4%, RR: 1.02, 95%CI: 0.95-1.09, and 46.3 versus 47.8, RR: 1.03, 95%CI: 0.95-1.12 respectively). A weak association in favor of aspirin administration was found in the matched cohort (49.5% versus 55.4%, RR: 1.12, 95%CI: 1.01-1.24, and 41.9% versus 47.8%, RR: 1.14, 95%CI: 1.01-1.29 respectively). However, when stratified the patients with WHO BMI criteria, a significant increase in live birth rate associated with aspirin treatment was found only in patients with low BMI (<18.5 kg/m2) in either unmatched (46.4% versus 59.8%, RR:1.29, 95%CI:1.07-1.55) or matched cohort (44% versus 59.8%, RR: 1.36, 95%CI: 1.01-1.83) but not in patients with higher BMI categories. With the interaction analysis, less association between aspirin and live birth appeared in patients with normal BMI (Ratio of OR:0.49, 95%CI: 0.29-0.81) and high BMI (Ratio of OR:0.57, 95%CI: 0.27-1.2) compared with patients with low BMI. Conclusion: BMI may be considered when evaluating aspirin's effect in FET cycles.

Evaluation of Dynamic Changes of Volatile Organic Components for Fishmeal during Storage by HS-SPME-GC-MS with PLS-DA.

Headspace solid-phase microextraction, combined with gas chromatography-mass spectrometry and partial least squares discriminant analysis, was adopted to study the rule of change in volatile organic compounds (VOCs) for domestic and imported fishmeal during storage with different freshness grades. The results showed that 318 kinds of VOCs were detected in domestic fishmeal, while 194 VOCs were detected in imported fishmeal. The total relative content of VOCs increased with storage time, among which acids and nitrogen-containing compounds increased significantly, esters and ketones increased slightly, and phenolic and ether compounds were detected only in domestic fishmeal. Regarding the volatile base nitrogen, acid value, pH value, and mold counts as freshness indexes, the freshness indexes were significantly correlated with nine kinds of VOCs (p < 0.05) through the correlation analysis. Among them, volatile base nitrogen had a significant correlation with VOCs containing nitrogen, acid value with VOCs containing carboxyl group and hydrocarbons, pH value with acids which could be used to adjust pH value, and mold counts with part of acids adjusting pH value and VOCs containing nitrogen. Due to the fact that the value of all freshness indexes increased with freshness degradation during storage, based on volatile base nitrogen and acid value, the fishmeal was divided into three freshness grades, superior freshness, corrupting, and completely corrupted. By using partial least squares discriminant analysis, this study revealed the differences in flavor of the domestic and imported fishmeal during storage with different freshness grades, and it identified four common characteristic VOCs, namely ethoxyquinoline, 6,7,8,9-tetrahydro-3H-benzo[e]indole-1,2-dione, hexadecanoic acid, and heptadecane, produced by the fishmeal samples during storage, as well as the characteristic VOCs of fishmeal at each freshness grade.

A simple protocol to establish a conditionally immortalized mouse podocyte cell line.

Podocytes are specialized terminally differentiated cells in the glomerulus that are the primary target cells in many glomerular diseases. However, the current podocyte cell lines suffer from prolonged in vitro differentiation and limited survival time, which impede research progress. Therefore, it is necessary to establish a cell line that exhibits superior performance and characteristics. We propose a simple protocol to obtain an immortalized mouse podocyte cell (MPC) line from suckling mouse kidneys. Primary podocytes were cultured in vitro and infected with the SV40 tsA58 gene to obtain immortalized MPCs. The podocytes were characterized using Western blotting and quantitative real-time PCR. Podocyte injury was examined using the Cell Counting Kit-8 assay and flow cytometry. First, we successfully isolated an MPC line and identified 39 °C as the optimal differentiation temperature. Compared to undifferentiated MPCs, the expression of WT1 and synaptopodin was upregulated in differentiated MPCs. Second, the MPCs ceased proliferating at a nonpermissive temperature after day 4, and podocyte-specific proteins were expressed normally after at least 15 passages. Finally, podocyte injury models were induced to simulate podocyte injury in vitro. In summary, we provide a simple and popularized protocol to establish a conditionally immortalized MPC, which is a powerful tool for the study of podocytes.

Vericiguat improves cardiac function and microcirculation of a male patient with Fabry disease: A case report.

Fabry disease (FD) is a rare X chromosome-linked disorder and can be easily misdiagnosed. Here, we report the case of a 69-year-old male patient with FD who developed heart failure and showed extremely high pulmonary artery pressure. His initial symptom was recurrent atrial fibrillation. The left and right atrial inner diameters were large, and the ventricular wall was thick. Gene analysis which showed GLA c.215T>C p.Met72Thr mutation and single photon emission computed tomography indicated the diagnosis of FD with coronary microvascular dysfunction. The patient was prescribed anti-heart failure drugs, including vericiguat. Following the treatment, his heart function and microvascular perfusion significantly improved, which might be due to the beneficial effects of vericiguat.

Prediction of anti-cancer drug synergy based on cross-matching network and cancer molecular subtypes.

At present, anti-cancer drug synergy therapy is one of the most important methods to overcome drug resistance and reduce drug toxicity in cancer treatment. High-throughput screening through deep learning can effectively improve the efficiency of discovering synergistic drugs. Nowadays, most of the existing deep learning algorithms for anti-cancer drug synergy prediction use deep neural networks and can only implicitly perform feature interaction. This study proposes a deep learning algorithm, named MolCross, which combines implicit feature interaction with explicit features to improve the accuracy of prediction of the anti-cancer drug synergy score. MolCross uses a deep autoencoder to extract features from high-dimensional input, uses the drug-specific subnetworks and cross-network to perform implicit feature interaction and explicit feature interaction respectively, and finally uses a synergy prediction network to combine the two feature interaction methods to obtain the final prediction results. We adopted a five-fold cross validation and compared MolCross with other four anti-cancer drug synergy prediction models. The results show that MolCross has better prediction performance than other models. MolCross also has good performance in terms of cross-cell line and cross-tissue type. Existing studies have demonstrated that cancer molecular subtypes have different sensitivities to targeted therapy. In this study, the features of cancer molecular subtype were introduced in the model using an embedding layer in MolCross to explore the effect of cancer molecular subtype on anti-cancer drug synergy. We also found that the cancer molecular subtype is one of the main factors affecting the synergy between drugs.

Paramyosin from field snail (Bellamya quadrata): Structural characteristics and its contribution to enhanced the gel properties of myofibrillar protein.

To assess the blending effect of field snails with grass carp muscle, the effects of paramyosin (PM) and actomyosin (AM) with different mixture ratios on the gel properties of the binary blend system were investigated in our work. The purified PM from field snail muscle was about 95 kDa on SDS-PAGE. Its main secondary structure was α-helix, which reached to 97.97 %. When the amount of PM increased in the binary blend system, their rheological indices and gel strength were improved. The water holding capacity (WHC) increased to 86.30 % at a mixture ratio of 2:8. However, the WHC and the area of immobile water (P22) dramatically decreased, and the area of free water (P23) increased when the mixture ratio exceeded 4:6. The low level of PM in binary blend system promoted the formation of a homogenous and dense gel network through non-covalent interactions as observed results of SEM and FTIR. When there were redundant PM molecules, the development of heterostructure via hydrophobic interaction of tail-tail contributed to the reduced gel properties of the binary blend system. These findings provided new insight into the binary blend system of PM and AM with different ratios to change the gel properties of myofibrillar protein.

Indications affect neonatal outcomes following early rescue ICSI: a retrospective study.

PURPOSE: To investigate the impact of heterogeneity in patient indications or insemination protocols on neonatal outcomes of singletons following early rescue ICSI (rICSI) treatments. METHODS: A retrospective study was conducted. Propensity score matching and multivariable logistic regression were used to adjust for confounders and biases. RESULTS: A total of 9095 IVF patients, 2063 ICSI patients, and 642 early rICSI patients were included in the study. No differences were detected in neonatal outcomes except small for gestational age (SGA) which increased in early rICSI patients compared with both unmatched and matched IVF groups with the risk ratio (RR) of 1.31 (95% CI: 1.05, 1.64) and 1.49 (95% CI: 1.05, 2.12). Further analysis showed that SGA increased significantly in partial fertilization failure (PFF) cycles with RRs of 1.56 (95% CI: 1.08, 2.27) and 1.78 (95% CI: 1.22, 2.59) compared with both unmatched and matched IVF patients but not in TFF patients. A positive association between fertilization rate via IVF and birth weight z-score was revealed in the PFF patients. CONCLUSION: Early rICSI in patients with total fertilization failure (TFF) appeared to be safe in terms of neonatal outcomes. However, when expanding the indications of rICSI to PFF patients, the SGA in the offspring increased, suggesting a potential effect on long-term health. Since other treatment options, such as using only the IVF-origin embryos still exist for these patients, further studies were needed to confirm the optimal decision for these patients.

Gene signatures to therapeutics: Assessing the potential of ivermectin against t(4;14) multiple myeloma.

BACKGROUND: Multiple myeloma (MM) is a terminal differentiated B-cell tumor disease characterized by clonal proliferation of malignant plasma cells and excessive levels of monoclonal immunoglobulins in the bone marrow. The translocation, (t)(4;14), results in high-risk MM with limited treatment alternatives. Thus, there is an urgent need for identification and validation of potential treatments for this MM subtype. Microarray data and sequencing information from public databases could offer opportunities for the discovery of new diagnostic or therapeutic targets. AIM: To elucidate the molecular basis and search for potential effective drugs of t(4;14) MM subtype by employing a comprehensive approach. METHODS: The transcriptional signature of t(4;14) MM was sourced from the Gene Expression Omnibus. Two datasets, GSE16558 and GSE116294, which included 17 and 15 t(4;14) MM bone marrow samples, and five and four normal bone marrow samples, respectively. After the differentially expressed genes were identified, the Cytohubba tool was used to screen for hub genes. Then, the hub genes were analyzed using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis. Using the STRING database and Cytoscape, protein-protein interaction networks and core targets were identified. Potential small-molecule drugs were identified and validated using the Connectivity Map database and molecular docking analysis, respectively. RESULTS: In this study, a total of 258 differentially expressed genes with enriched functions in cancer pathways, namely cytokine receptor interactions, nuclear factor (NF)-κB signaling pathway, lipid metabolism, atherosclerosis, and Hippo signaling pathway, were identified. Ten hub genes (cd45, vcam1, ccl3, cd56, app, cd48, btk, ccr2, cybb, and cxcl12) were identified. Nine drugs, including ivermectin, deforolimus, and isoliquiritigenin, were predicted by the Connectivity Map database to have potential therapeutic effects on t (4;14) MM. In molecular docking, ivermectin showed strong binding affinity to all 10 identified targets, especially cd45 and cybb. Ivermectin inhibited t(4;14) MM cell growth via the NF-κB pathway and induced MM cell apoptosis in vitro. Furthermore, ivermectin increased reactive oxygen species accumulation and altered the mitochondrial membrane potential in t(4;14) MM cells. CONCLUSION: Collectively, the findings offer valuable molecular insights for biomarker validation and potential drug development in t(4;14) MM diagnosis and treatment, with ivermectin emerging as a potential therapeutic alternative.

An effective framework for predicting drug-drug interactions based on molecular substructures and knowledge graph neural network.

Drug-drug interactions (DDIs) play a central role in drug research, as the simultaneous administration of multiple drugs can have harmful or beneficial effects. Harmful interactions lead to adverse reactions, some of which can be life-threatening, while beneficial interactions can promote efficacy. Therefore, it is crucial for physicians, patients, and the research community to identify potential DDIs. Although many AI-based techniques have been proposed for predicting DDIs, most existing computational models primarily focus on integrating multiple data sources or combining popular embedding methods. Researchers often overlook the valuable information within the molecular structure of drugs or only consider the structural information of drugs, neglecting the relationship or topological information between drugs and other biological objects. In this study, we propose MSKG-DDI - a two-component framework that incorporates the Drug Chemical Structure Graph-based component and the Drug Knowledge Graph-based component to capture multimodal characteristics of drugs. Subsequently, a multimodal fusion neural layer is utilized to explore the complementarity between multimodal representations of drugs. Extensive experiments were conducted using two real-world datasets, and the results demonstrate that MSKG-DDI outperforms other state-of-the-art models in binary-class, multi-class, and multi-label prediction tasks under both transductive and inductive settings. Furthermore, the ablation analysis further confirms the practical usefulness of MSKG-DDI.

Diagnostic value of serum dia in acute myocardial infarction and aortic dissection in athletic patients and those with optimal physical health

Objective: To explore the relationship between serum lipoprotein (a) levels and acute myocardial infarction (AMI) and aortic dissection in athletic patients and those with optimal physical health. Methods: This study involved 216 athletic patients admitted to a Chinese hospital for AMI who underwent Percutaneous Coronary Intervention (PCI) between 2018 and 2019. These patients, characterized by their athletic background and optimal physical health, were divided based on their serum lipoprotein (a) levels: 133 in the low-lipoprotein (a) group (<300 mg/L) and 83 in the high-lipoprotein (a) group (≥300 mg/L). Data including baseline demographics, laboratory tests, and details of interventional treatment were collected from medical records. All patients were followed up for two years post-discharge to record Major Adverse Cardiac Events (MACE). Factors influencing MACE were analyzed using univariate and multivariate logistic regression. Results: The low lipoprotein (a) group exhibited lower age, reduced Killip grades III-IV, lower LDL-C levels, and fewer diseased vessels than the high lipoprotein (a) group (P><0.05). The incidence of MACE was significantly lower in the low lipoprotein (a) group (5.3%, 7/133) compared to the high lipoprotein (a) group (27.87%, 51/183) (P><0.05). Univariate analysis identified significant differences in age, post-surgery β-blocker use, LDL-C levels, serum lipoprotein (a) levels, revascularization strategies, and the> <3 00 mg/L) and 83 in the high-lipoprotein (a) group (≥300 mg/L). Data including baseline demographics, laboratory tests, and details of interventional treatment were collected from medical records. All patients were followed up for two years post-discharge to record Major Adverse Cardiac Events (MACE). Factors influencing MACE were analyzed using univariate and multivariate logistic regression (AU)

ConPep: Prediction of peptide contact maps with pre-trained biological language model and multi-view feature extracting strategy.

The accurate prediction of peptide contact maps remains a challenging task due to the difficulty in obtaining the interactive information between residues on short sequences. To address this challenge, we propose ConPep, a deep learning framework designed for predicting the contact map of peptides based on sequences only. To sufficiently incorporate the sequential semantic information between residues in peptide sequences, we use a pre-trained biological language model and transfer prior knowledge from large scale databases. Additionally, to extract and integrate sequential local information and residue-based global correlations, our model incorporates Bidirectional Gated Recurrent Unit and attention mechanisms. They can obtain multi-view features and thus enhance the accuracy and robustness of our prediction. Comparative results on independent tests demonstrate that our proposed method significantly outperforms state-of-the-art methods even with short peptides. Notably, our method exhibits superior performance at the sequence level, suggesting the robust ability of our model compared with the multiple sequence alignment (MSA) analysis-based methods. We expect it can be meaningful research for facilitating the wide use of our method.

Prediction of cancer recurrence based on compact graphs of whole slide images.

Cancer recurrence is one of the primary causes of patient mortality following treatment, indicating increased aggressiveness of cancer cells and difficulties in achieving a cure. A critical step to improve patients' survival is accurately predicting recurrence status and giving appropriate treatment. Whole Slide Images (WSIs) are a common type of image data in the field of digital pathology, containing high-resolution tissue information. Furthermore, WSIs of primary tumors contain microenvironmental information directly associated with the growth of tumor cells. To effectively utilize this microenvironmental information. Firstly, we represented microenvironmental features of histopathological images as compact graphs. Secondly, this work aims to develop an enhanced lightweight graph neural network called the Adaptive Graph Clustering Network (AGCNet) for predicting cancer recurrence. Experiments are conducted on three cancer datasets from The Cancer Genome Atlas (TCGA), and AGCNet achieved an accuracy of 81.81% in BLCA, 69.66% in PAAD, and 81.96% in STAD. These results indicated that AGCNet is an effective model for predicting cancer recurrence and is expected to be applied in clinical applications.

RETRACTED ARTICLE: Analysis of fractured soft rock characteristics in fault rupture zones and laneway shoring.

Fault rupture is a common phenomenon in geotechnical engineering. To prevent rupture, laneway shoring is performed, prior to which, convergence deformation, failure criteria, and fracture development in soft rocks in the fault rupture zone are carefully analyzed. Then, a supporting structure corresponding to the actual situation of the soft rock in the rupture zone is created. Herein, the water-rich laneway shoring through the fault rupture zone of the Hongqingliang coal mine located in the Inner Mongolia Autonomous Region is taken as the research object. Then, the fracture development and characteristics of argillaceous siltstones and laneway shoring cross-fault rupture zone are studied. Site inspection, indoor and field tests, theoretical analysis, numerical simulation, and field monitoring were used for systematic fracture analysis. Results indicated that laneway shoring through the fault fracture zone in the Hongqingliang coal mine could help prevent disasters. This method was extended to laneway supports built through the fault rupture zones in mines in other areas of China.

Preparation and structural analysis of fucomannogalactan and ß-1,6-glucan from Grifola frondosa mycelium.

Introduction: Polysaccharides, key components present in Grifola frondosa, can be divided into those derived from fruiting bodies, mycelium, and fermentation broth based on their source. The structure of G. frondosa fruiting body-derived polysaccharides has been fully characterized. However, the structure of G. frondosa mycelium-derived polysaccharides remains to be elucidated. Methods: In this study, we obtained mycelia from G. frondosa by liquid fermentation and extracted them with water and alkaline solution. Then, the mycelia were isolated and purified to obtain homogeneity and systematically characterized by methylation and FT infrared (FT-IR) and nuclear magnetic resonance (NMR) spectroscopy. Results and discussion: Structural analysis showed that two neutral fractions (WGFP-N-a and AGFP-N-a1) have a common backbone composed of α-1,6-D-Me-Galp and α-1,6-D-Galp that were substituted at O-2 by 1,2-Manp, α-1,3-L-Fucp, and α-T-D-Manp and thus are identified as fucomannogalactans. WGFP-A-a, AGFP-A-b, and AGFP-A-c are ß-1,6-glucans with different molecular weights and are branched with ß-1,3-D-Glcp and T-D-Glcp at the O-3 of Glc. Our results provide important structural information about G. frondosa mycelium-derived polysaccharides and provide the basis for their further development and application.

Distinct mutational processes shape selection of MHC class I and class II mutations across primary and metastatic tumors.

Disruption of antigen presentation via loss of major histocompatibility complex (MHC) expression is a strategy whereby cancer cells escape immune surveillance and develop resistance to immunotherapy. Here, we develop the personalized genomics algorithm Hapster and accurately call somatic mutations within the MHC genes of 10,001 primary and 2,199 metastatic tumors, creating a catalog of 1,663 non-synonymous mutations that provide key insights into MHC mutagenesis. We find that MHC class I genes are among the most frequently mutated genes in both primary and metastatic tumors, while MHC class II mutations are more restricted. Recurrent deleterious mutations are found within haplotype- and cancer-type-specific hotspots associated with distinct mutational processes. Functional classification of MHC residues reveals significant positive selection for mutations disruptive to the B2M, peptide, and T cell binding interfaces, as well as to MHC chaperones.

Identification of immunity-related lncRNAs and construction of a ceRNA network of potential prognostic biomarkers in acute myeloid leukemia.

Objective: Using bioinformatics analyses, this study aimed to identify lncRNAs related to the immune status of acute myeloid leukemia (AML) patients and ascertain the potential impact in immunity-related competing endogenous RNA (ceRNA) networks on AML prognosis. Methods: AML-related RNA-seq FPKM data, AML-related miRNA expression microarray data, and gene sets associated with immunity-related pathways were, respectively, obtained from the TCGA, GEO, and ImmReg databases. An immunity-related ceRNA network was then constructed according to the predicted interactions between AML-related mRNAs, lncRNAs, and miRNAs. After performing LASSO and multivariate Cox regression analyses, lncRNAs in the ceRNA network were used to establish an AML prognostic model. According to mutual regulatory relationships and consistent trends of expression among candidate ceRNAs, two ceRNA subnetworks related to the AML prognostic model were determined. Finally, the correlation between the expression levels of mRNAs, lncRNAs, and miRNAs in each ceRNA subnetwork and immune cell infiltration (assessed by combining the ESTIMATE and CIBERSORT methods and ssGSEA) was analyzed. Results: A total of 424 immunity-related differentially expressed (IR-DE) mRNAs (IR-DEmRNAs), 191 IR-DElncRNAs, and 69 IR-DEmiRNAs were obtained, and a ceRNA network of 20 IR-DElncRNAs, 6 IR-DEmRNAs, and 3 IR-DEmiRNAs was established. Univariate Cox regression analysis was conducted on 20 IR-DElncRNAs, and 7 of these were identified to be significantly correlated with the overall survival (OS) time in AML patients. Then, two IR-DElncRNAs (MEG3 and HCP5) were screened as independent OS-related factors by LASSO and multivariable Cox regression analyses, and a prognostic model was constructed to evaluate the survival risk in AML patients. Survival analyses indicated that the OS of patients was often poor in the high-risk group. Additionally, from this model, two ceRNA regulatory pathways, namely, MEG3/miR-125a-5p/SEMA4C and HCP5/miR-125b-5p/IL6R, which were potentially involved in the immune regulation of AML prognosis were identified. Conclusion: lncRNAs HCP5 and MEG3 may act as key ceRNAs in the pathogenesis in AML by regulating immune cell representation as part of the regulatory lncRNA-miRNA-mRNA axes. The candidate mRNAs, lncRNAs, and miRNAs included in the ceRNA network identified here may serve as useful prognostic biomarkers and immunotherapeutic targets for AML.

miR-125b-5p alleviates the damage of myocardial infarction by inhibiting the NFAT2 to reduce F2RL2 expression.

Aim: To explore the effect of miR-125b-5p/nuclear factor of activated T cells 1 (NFAT2)/F2RL2 on myocardial infarction (MI). Method: After establishment of MI mouse model and oxygen glucose deprivation (OGD)-induced cell model, the effects of NFAT2 on the process of MI were observed, the effects of miR-125b-5p/NFAT2/F2RL2 on the cell viability, apoptosis, and inflammatory factors levels were determined. Result: NFAT2 silencing relieved MI and inhibited the inflammation in MI model mice. In OGD-induced human coronary artery endothelial cells and human cardiac microvascular endothelial cells, miR-125b-5p enhanced cell viability, yet repressed cell apoptosis and inflammatory factors and NFAT2 levels. NFAT2 overexpression reversed the effects of miR-125b-5p, while F2RL2 silencing offset the effects of NFAT2 overexpression. Conclusion: MiR-125b-5p alleviates MI injury by inhibiting NFAT2 level to reduce F2RL2 expression.

This research proves that miR-125b-5p reduces the level of F2RL2 by preventing the activation of NFAT2 pathway, thereby reducing cardiogenic vascular endothelial cell damage and inflammation (heat, swelling and redness). This may provide a new treatment for heart attacks.